scholarly journals Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

2020 ◽  
Author(s):  
Margaret R. Krause ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Ravi P. Singh ◽  
Francisco Pinto ◽  
...  
Keyword(s):  
Grain Yield ◽  
High Throughput ◽  
Vegetation Indices ◽  
Early Generation ◽  
Breeding Programs ◽  
Breeding Lines ◽  
High Throughput Phenotyping ◽  
Seed Increase ◽  
Yield Trials ◽  
Selection Of

ABSTRACTBreeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

scholarly journals Aerial high‐throughput phenotyping enables indirect selection for grain yield at the early generation, seed‐limited stages in breeding programs

Crop Science ◽  
2020 ◽  
Vol 60 (6) ◽  
pp. 3096-3114 ◽  
Author(s):  
Margaret R. Krause ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Ravi P. Singh ◽  
Francisco Pinto ◽  
...  
Keyword(s):  
Grain Yield ◽  
High Throughput ◽  
Indirect Selection ◽  
Early Generation ◽  
Breeding Programs ◽  
High Throughput Phenotyping ◽  
Selection For

Early-Generation Yield Trials of Peanuts1

1974 ◽  
Vol 1 (1) ◽  
pp. 3-6 ◽  
Author(s):  
Terry A. Coffelt ◽  
Ray O. Hammons
Keyword(s):  
Arachis Hypogaea ◽  
Seed Weight ◽  
Breeding Program ◽  
Early Generation ◽  
Breeding Lines ◽  
Early Testing ◽  
Yield Trials ◽  
Selection Of

Abstract In 1969, a large number of F2 lines developed from reciprocal infraspecific crosses between the varieties ‘Argentine’ and ‘Early Runner’ was available for use in the peanut (Arachis hypogaea) breeding program at Tifton, Georgia. These lines were used to make preliminary observations on the possible use of early-generation yield trials in developing superior peanut varieties. High yielding F2 lines were tested consecutively in F3, F4, F5 and F6 yield trials. Lines not out-yielding the parental cultivars were discarded after each test. Lines were placed in F5 and F6 Spanish and Runner yield trials on the basis of seed weight/100 seed. Eight commercial checks were used in the F5 yield trials and five in the F6 yield trials. Yield and shelling grade data from the F5 and F6 yield trials were evaluated statistically. Nine of the twelve breeding lines in the F5 yield trials outylelded the parents. Seven yielded more than the highest yielding commercial check. Two of the five breeding lines in the F6 yield trials did not yield significantly less than the highest yielding commercial check. The remaining three did not yield significantly less than the parental cultivars. Based on these results, early testing in yield trials may be an acceptable breeding procedure for evaluation and selection of peanut varieties.

scholarly journals Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat

2016 ◽  
Vol 6 (9) ◽  
pp. 2799-2808 ◽  
Author(s):  
Jessica Rutkoski ◽  
Jesse Poland ◽  
Suchismita Mondal ◽  
Enrique Autrique ◽  
Lorena González Pérez ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Selection ◽  
High Throughput ◽  
Vegetation Indices ◽  
Canopy Temperature ◽  
Improve Accuracy ◽  
High Throughput Phenotyping ◽  
Selection For

scholarly journals Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology

2022 ◽  
Vol 12 ◽  
Author(s):  
Jing Zhou ◽  
Eduardo Beche ◽  
Caio Canella Vieira ◽  
Dennis Yungbluth ◽  
Jianfeng Zhou ◽  
...  
Keyword(s):  
High Throughput ◽  
Multispectral Imaging ◽  
Large Population ◽  
Image Features ◽  
Environment Interaction ◽  
Breeding Programs ◽  
Soybean Yield ◽  
Yield Trial ◽  
High Throughput Phenotyping ◽  
Selection Of

The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.

scholarly journals High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

2021 ◽  
Author(s):  
Marcus Vinicius Vieira Borges ◽  
Janielle de Oliveira Garcia ◽  
Tays Silva Batista ◽  
Alexsandra Nogueira Martins Silva ◽  
Fabio Henrique Rojo Baio ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Throughput ◽  
Mean Squared Error ◽  
Vegetation Indices ◽  
Plant Size ◽  
Eucalyptus Species ◽  
Forest Inventories ◽  
Spectral Bands ◽  
High Throughput Phenotyping ◽  
Input Variables

AbstractIn forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

scholarly journals Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Tang ◽  
Atit Parajuli ◽  
Chunpeng James Chen ◽  
Yang Hu ◽  
Samuel Revolinski ◽  
...  
Keyword(s):  
Prediction Model ◽  
High Throughput ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Selection Process ◽  
Forage Legume ◽  
Breeding Programs ◽  
High Throughput Phenotyping ◽  
Fully Automatic ◽  
Uav Images

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage

2019 ◽  
Vol 132 (6) ◽  
pp. 1705-1720 ◽  
Author(s):  
Jin Sun ◽  
Jesse A. Poland ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Philomin Juliana ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Selection ◽  
High Throughput ◽  
Early Stage ◽  
Wheat Grain ◽  
High Throughput Phenotyping ◽  
Selection For

scholarly journals Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

2020 ◽  
Vol 12 (6) ◽  
pp. 998 ◽  
Author(s):  
GyuJin Jang ◽  
Jaeyoung Kim ◽  
Ju-Kyung Yu ◽  
Hak-Jin Kim ◽  
Yoonha Kim ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
New Technologies ◽  
Cost Effective ◽  
New Wave ◽  
Breeding Programs ◽  
Modern Agriculture ◽  
Aerial Vehicle ◽  
High Throughput Phenotyping

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

scholarly journals Tolerance to preharvest sprouting and yield of wheat genotypes from different breeding programs

2015 ◽  
Vol 50 (8) ◽  
pp. 698-706
Author(s):  
Rafael Nörnberg ◽  
José Antonio Gonzalez da Silva ◽  
Henrique de Souza Luche ◽  
Elisane Weber Tessmann ◽  
Sydney Antonio Frehner Kavalco ◽  
...  
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Preharvest Sprouting ◽  
Rio Grande Do Sul ◽  
Falling Number ◽  
Breeding Programs ◽  
Wheat Genotypes ◽  
Randomized Complete Block Design ◽  
Superior Genotypes ◽  
Selection Of

Abstract:The objective of this work was to characterize the performance of elite wheat genotypes from different Brazilian breeding programs for traits associated with grain yield and preharvest sprouting. The study was conducted in 2010 and 2011 in the municipality of Capão do Leão, in the state of Rio Grande do Sul, Brazil, in a randomized complete block design with three replicates. Thirty-three wheat genotypes were evaluated for traits related to preharvest sprouting and grain yield. The estimate of genetic distance was used to predict potential combinations for selection of plants with high grain yield and tolerance to preharvest sprouting. The combined analysis of sprouted grains and falling number shows that the TBIO Alvorada, TBIO Mestre, Frontana, Fundacep Raízes, Fundacep Cristalino, and BRS Guamirim genotypes are tolerant to preharvest sprouting. Combinations of TBIO Alvorada and TBIO Mestre with Fundacep Cristalino show high potential for recovering superior genotypes for high grain yield and tolerance to preharvest sprouting.

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